Background

This document sets out a few practical recipes for modelling with (life) insurance data. Insurance events are typically of low probability - these recipes consider some of the limitations of “small data” model fitting (where the observations of interest are sparse) and other topics for insurance like comparisons to standard tables. Themes

• Common data transforms, summary stats, and simple visualisations
• Regression
  • Grouped vs ungrouped data
  • Choice of: response distribution, link (and offsets), explanatory variables
  • Modelling variance to industry/ reference (A/E or A - E)
  • Model selection: stepwise regression, likelihood tests, model evaluation
  • Predictions, confidence intervals and visualisations
• Bayesian regression and other classification models - to follow.

See link above to GitHub repository which has the detailed code.